Enhanced generative adversarial network for extremely imbalanced fault diagnosis of rotating machine

Abstract

Fault diagnosis is the key procedure to ensure the stability and reliability of mechanical equipment operation. Recent works show that deep learning-based methods outperform most of traditional fault diagnosis techniques. However, a practical problem comes up in these studies, where deep learning models cannot be well trained and the classification accuracy is greatly affected because of the sample-imbalance problem, which means that there are a large amount of normal data but few fault samples. To solve the problem, an enhanced generative adversarial network (E-GAN) is proposed. Firstly, the deep convolutional generative adversarial network (DCGAN) is utilized to generate more samples to balance the training set. Then, by integrating K-means clustering algorithm, we developed a modified CNN diagnosis model for fault classification. The experiment results demonstrate that the proposed E-GAN can greatly improve the classification accuracy and is superior to the compared methods.